About

Runtime Architecture for Stable, Long-Horizon AI Systems

Open standard. Proprietary engine. Research foundations.

What is Sigma Stratum

Sigma Stratum defines the architectural foundation for stable, long-horizon AI systems.

It serves as the umbrella framework behind the Sigma Runtime Standard (SRS) and Sigma Runtime, establishing how AI systems can operate with bounded reasoning, measurable stability, and controlled recursion across extended interactions.

Sigma Stratum is not a model. It is a runtime architecture layer that formalizes how stability, context integrity, and reasoning invariants are maintained in complex AI workflows.

Sigma Runtime Standard (SRS)

The Sigma Runtime Standard (SRS) is an open framework for bounded reasoning and cognitive stability in AI systems.

SRS formalizes:

- Drift detection and control mechanisms.

- Recursive reasoning invariants.

- Context persistence and anchor integrity.

- Runtime-level constraint enforcement.

The standard is vendor-agnostic and model-agnostic. It does not require modification of model weights. Instead, it defines how external runtime layers structure interaction dynamics to ensure stability across long reasoning chains.

SRS documentation and specifications are publicly available.

Sigma Runtime

Sigma Runtime is the proprietary reference implementation of the Sigma Runtime Standard.

It operates as an external runtime layer that:

- Bounds reasoning drift.

- Preserves contextual integrity across extended workflows.

- Enforces deterministic behavioral constraints.

- Enables mission-critical AI deployment.

Sigma Runtime is designed for environments where consistency, auditability, and rule integrity must hold across multi-step reasoning processes.

The runtime is compatible with modern large language models and does not require fine-tuning. Stability is achieved through structured recursion, memory orchestration, and constraint-aware interaction control.

Research Foundations

Sigma Stratum builds on research in neurosymbolic architectures, recursive cognition, and system-level AI design.

These foundations inform the runtime architecture, translating theoretical models of cognitive stability into production-grade infrastructure for long-horizon AI systems.

The research program continues to explore formal stability metrics, recursive coherence modeling, and runtime-level observability for complex AI environments.

Vision

Sigma Stratum aims to define the runtime architecture layer that makes long-horizon AI systems stable, auditable, and deployable at scale.

By separating open standards from proprietary implementation, Sigma Stratum establishes a foundation where AI systems can operate under bounded reasoning constraints while remaining adaptable, extensible, and model-agnostic.